To discover new lead compounds for drug design, large libraries of chemicals are screened for their ability to modulate the function of a particular target biomacromolecule (e.g., a receptor, or enzyme). High-throughput screening (HTS) and virtual screening are two methods of discovering new lead compounds that are currently widely employed in drug design. In the latter case, the computational “hits” are then verified experimentally.
These screening methods have been used to identify novel molecules that are dissimilar to known ligands of the specific target, but nevertheless bind to that target at micromolar or even sub-micromolar concentrations. However, often, on detailed investigation, many of the novel “hits” are found to be false positives, ie., the inhibitor is found to be non-specific. A hit can be categorized as a “false positive” for many reasons. Among the largest classes of false positives are those molecules that act non-specifically, that have undefined mechanisms of inhibition, and/or that have unusual and undesired kinetic properties. Such non-specific inhibitors artificially inflate hit rates in both virtual and experimental screening projects, and lead investigators to follow the wrong trail. This can waste enormous time and resources. Unfortunately, heretofore, there has been no reliable method of identifying a compound as a non-specific inhibitor.
There is a need to provide a method of detecting false positives early in the discovery process methodology. In addition, there is a need to provide compound/screening libraries that are essentially free of compounds that lead to such false positives, as well as methods that can be used to identify such compounds in a drug library.